Conditional Graph Information Bottleneck for Molecular Relational Learning (2305.01520v2)

Abstract: Molecular relational learning, whose goal is to learn the interaction behavior between molecular pairs, got a surge of interest in molecular sciences due to its wide range of applications. Recently, graph neural networks have recently shown great success in molecular relational learning by modeling a molecule as a graph structure, and considering atom-level interactions between two molecules. Despite their success, existing molecular relational learning methods tend to overlook the nature of chemistry, i.e., a chemical compound is composed of multiple substructures such as functional groups that cause distinctive chemical reactions. In this work, we propose a novel relational learning framework, called CGIB, that predicts the interaction behavior between a pair of graphs by detecting core subgraphs therein. The main idea is, given a pair of graphs, to find a subgraph from a graph that contains the minimal sufficient information regarding the task at hand conditioned on the paired graph based on the principle of conditional graph information bottleneck. We argue that our proposed method mimics the nature of chemical reactions, i.e., the core substructure of a molecule varies depending on which other molecule it interacts with. Extensive experiments on various tasks with real-world datasets demonstrate the superiority of CGIB over state-of-the-art baselines. Our code is available at https://github.com/Namkyeong/CGIB.

• The paper presents the CGIB framework that conditionally extracts core molecular subgraphs to enhance predictive accuracy for interaction tasks.
• It employs the Information Bottleneck principle to focus on task-relevant substructures while pruning non-essential graph components.
• Experimental results demonstrate CGIB’s robustness in both transductive and inductive settings, outperforming strong baseline models.

Conditional Graph Information Bottleneck for Molecular Relational Learning

The paper "Conditional Graph Information Bottleneck for Molecular Relational Learning" introduces the Conditional Graph Information Bottleneck (CGIB) framework, a novel approach to molecular relational learning that aims to accurately predict interaction behaviors between molecular pairs by identifying crucial substructures within them. This method enhances the predictive ability of molecular interaction models by focusing on segments of molecular graphs that are most relevant for a given task, leveraging concepts from the Information Bottleneck (IB) theory.

Overview of Molecular Relational Learning and Current Challenges

Molecular relational learning models the interactions within molecular pairs, translating these interactions into graph representations where atoms are nodes, and bonds are edges. Graph Neural Networks (GNNs) have previously been used in this field, but they often fail to account adequately for substructures like functional groups which define the chemical reactions of molecules. For instance, in chemical contexts such as drug interactions or solubility predictions, only certain substructures may influence the reaction and need to be detected dynamically based on interacting molecules. The new CGIB paradigm directly addresses this by allowing the core substructure of a molecule to adapt based on its interaction partner.

Conditional Graph Information Bottleneck Framework

CGIB builds upon the IB principle to conditionally learn core subgraphs that remain invariant to the irrelevant molecular components. The methodology involves maximizing the mutual information between the core subgraph and the prediction target while minimizing the shared information between the original graph and its contracted subgraph, conditioned on its interaction pair. This approach dynamically discerns the task-specific core substructure by exploiting the chain rule of mutual information, integrating Gaussian noise to prune less critical parts of the graph representation.

Experimental Validation and Results

Empirical validation on various real-world datasets demonstrates the superiority of CGIB over existing models, particularly against strong baselines such as CIGIN for molecular interaction tasks, and SSI-DDI for drug-drug interactions. The datasets involved molecular properties like solubility and drug interactions, where CGIB consistently enhances predictive performance. Notably, the research highlights the robustness of CGIB in both transductive and inductive settings, showcasing its generalization capabilities in scenarios where interaction pairs are entirely new graph instances not encountered during training.

Practical and Theoretical Implications

CGIB's ability to adaptively determine the core substructure of interacting molecules aligns closely with practical needs in molecular science, such as drug design, by offering a mechanistic insight into chemical interactions. Theoretically, CGIB extends the applicability of the Information Bottleneck principle to relational learning tasks, opening avenues for future research to explore conditional adaptations within diverse domains where relationships between entities must be contextually evaluated.

Future Directions

Looking ahead, the core insights from CGIB could be extended to more complex chemical systems or to other domains requiring relational mappings, such as social networks or biological systems. Additionally, examining different types of GNN architectures within the CGIB framework or integrating domain-specific knowledge regarding chemical properties could further enhance this method's utility.

By prioritizing task-specific molecular substructures in a relational context, CGIB provides a promising direction for more explainable and efficient molecular relational learning. This forward step can significantly impact specialized fields requiring precise interaction predictions, such as pharmaceuticals and materials science.